#coding=utf-8
#Word2Vec.py
import collections
import math
import os
import random
import zipfile
import numpy as np
import urllib
import tensorflow as tf
import matplotlib.pyplot as plt
#定义下载文本数据的函数，使用urllib.request.urlretrieve
# url = 'http://mattmahoney.net/dc/'
#
# def maybe_download(filename, expected_bytes):
#     if not os.path.exists(filename):
#         filename, _ = urllib.request.urlretrieve(url + filename, filename)
#     statinfo = os.stat(filename)
#     if statinfo.st_size == expected_bytes:
#         print('Found and verified', filename)
#     else:
#         print(statinfo.st_size)
#         raise Exception('Failed to verify' + filename + '. Can you get to it with a browser?')
#     return filename

#filename = maybe_download('text8.zip', 31344016)
filename = "C:\\Users\\xiaoyu\\PycharmProjects\\DeepLearning\\text8.zip"
#解压下载的压缩文件，并使用tf.compat.as_str将数据转化为单词的列表
def read_data(filename):
    with zipfile.ZipFile(filename) as f:
        data = tf.compat.as_str(f.read(f.namelist()[0])).split()
    return data
words = read_data(filename)
print('Data size', len(words))
#创建vocabulary词汇表
vocabulary_size = 50000

def build_dataset(words):
    count = [['UNK', -1]]
    count.extend(collections.Counter(words).most_common(vocabulary_size - 1))
    dictionary = dict()
    for word, _ in count:
        dictionary[word] = len(dictionary) #获取一个唯一的编号，相当于stl的map
    data = list()
    unk_count = 0
    for word in words:
        if word in dictionary:
            index = dictionary[word]
        else:
            index = 0
            unk_count += 1
        data.append(index)
    count[0][1] = unk_count
    reverse_dictionary = dict(zip(dictionary.values(), dictionary.keys()))
    return data, count, dictionary, reverse_dictionary

data, count, dictionary, reverse_dictionary = build_dataset(words)
#删除原始单词列表，再打印vocabulary中最高频出现的词汇和数量
del words
print('Most common words (+UNK)', count[:5])
print('Sample data', data[:10], [reverse_dictionary[i] for i in data[:10]])
#生成Word2Vec的样本
data_index = 0

def generate_batch(batch_size, num_skips, skip_window):
    global data_index
    assert batch_size % num_skips == 0
    assert num_skips <= 2 * skip_window
    batch = np.ndarray(shape=(batch_size), dtype=np.int32)
    labels = np.ndarray(shape=(batch_size, 1), dtype=np.int32)
    span = 2 * skip_window + 1
    buffer = collections.deque(maxlen=span)
    for _ in range(span):
        buffer.append(data[data_index])
        data_index = (data_index + 1) % len(data)
    for i in range(batch_size // num_skips):
        target = skip_window
        target_to_avoid = [skip_window]
        for j in range(num_skips):
            while target in target_to_avoid:
                target = random.randint(0, span - 1)
            target_to_avoid.append(target)
            batch[i * num_skips + j] = buffer[skip_window]
            labels[i * num_skips + j, 0] = buffer[target]
        buffer.append(data[data_index])
        data_index = (data_index + 1) % len(data)
    return batch, labels

#调用generate_batch函数简单测试一下功能
batch, labels = generate_batch(batch_size=8, num_skips=2, skip_window=1)
for i in range(8):
    print(batch[i], reverse_dictionary[batch[i]], '->', labels[i, 0], reverse_dictionary[labels[i, 0]])
#
batch_size = 128
embedding_size = 128
skip_window = 1
num_skips = 2

valid_size = 16
valid_window = 100
valid_examples = np.random.choice(valid_window, valid_size, replace=False)
num_sampled = 64
#定义Skip-Gram Word2Vec模型的网络结构
graph = tf.Graph()
with graph.as_default():
    train_inputs = tf.placeholder(tf.int32, shape=[batch_size])
    train_labels = tf.placeholder(tf.int32, shape=[batch_size, 1])
    valid_dataset = tf.constant(valid_examples, dtype=tf.int32)
    with tf.device('/cpu:0'):
        embeddings = tf.Variable(tf.random_uniform([vocabulary_size, embedding_size], -1.0, 1.0))
        embed = tf.nn.embedding_lookup(embeddings, train_inputs)
        nce_weights = tf.Variable(tf.truncated_normal([vocabulary_size, embedding_size], stddev=1.0/math.sqrt(embedding_size)))
        nce_biases = tf.Variable(tf.zeros([vocabulary_size]))
        loss = tf.reduce_mean(tf.nn.nce_loss(weights=nce_weights, biases=nce_biases, labels=train_labels, inputs=embed,
                                             num_sampled=num_sampled, num_classes=vocabulary_size))
        #定义优化器
        optimizer = tf.train.GradientDescentOptimizer(1.0).minimize(loss)
        norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keep_dims=True))
        normalized_embeddings = embeddings / norm
        valid_embeddings = tf.nn.embedding_lookup(normalized_embeddings, valid_dataset)
        similarity = tf.matmul(valid_embeddings, normalized_embeddings, transpose_b=True)

        init = tf.global_variables_initializer()

        num_steps = 100001
        with tf.Session(graph=graph) as session:
            init.run()
            print('Initalized')
            average_loss = 0
            for step in range(num_steps):
                batch_inputs, batch_labels = generate_batch(batch_size, num_skips, skip_window)
                feed_dict = {train_inputs : batch_inputs, train_labels : batch_labels}
                _, loss_val = session.run([optimizer, loss], feed_dict=feed_dict)
                average_loss += loss_val

                if(step % 2000 == 0):
                    if step > 0:
                        average_loss /= 2000
                    print("Average loss at step ", step, ": ", average_loss)
                    average_loss = 0
                if(step % 10000 == 0):
                    sim = similarity.eval()
                    for i in range(valid_size):
                        valid_word = reverse_dictionary[valid_examples[i]]
                        top_k = 8
                        nearest = (-sim[i, :]).argsort()[1: top_k+1]
                        log_str = "Nearest to %s:" % valid_word
                        for k in range(top_k):
                            close_word = reverse_dictionary[nearest[k]]
                            log_str = "%s %s, " % (log_str, close_word)
                        print(log_str)
                    final_embeddings = normalized_embeddings.eval()

def plot_with_labels(low_dim_embs, labels, filename='tsne.png'):
    assert low_dim_embs.shape[0] >= len(labels), "More labels than embeddings"
    plt.figure(figsize=(18, 18))
    for i, label in enumerate(labels):
        x, y = low_dim_embs[i, :]
        plt.scatter(x, y)
        plt.annotate(label, xy=(x, y), xytext=(5, 2), textcoords='offset points', ha='right', va='bottom')
    plt.savefig(filename)

from sklearn.manifold import TSNE
tsne = TSNE(perplexity=30, n_components=2, init='pca', n_iter=5000)
plot_only = 100
low_dim_embs = tsne.fit_transform(final_embeddings[:plot_only, :])
labels = [reverse_dictionary[i] for i in range(plot_only)]
plot_with_labels(low_dim_embs, labels)

